Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Forecast

arXiv:2404.16549v1 Announce Type: new
Abstract: Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around bridge piers based on historical sensor monitoring data, including riverbed elevation, flow elevation, and flow velocity. We investigated …

abstract application arxiv bridge challenge complexity convolutional neural networks cs.lg current forecast memory networks neural networks predictions process real-time type world